Method for dynamically training a system to determine an age rating of media content

A system for dynamically training a media classification system to determine an age rating for media content, the system comprising:

• a memory;
• and
• a processor configured to implement instructions on the memory to configure the media classification system to:
• receive a plurality of age ratings of a plurality of content items for a source territory, a target territory, and at least one other territory;
• determine, based on the plurality of age ratings, a prediction score indicative of a similarity between age ratings of the source territory and the target territory and at least one other prediction score indicative of a similarity between age ratings of the at least one other territory and the target territory;
• determine, from the source territory and the at least one other territory, a territory associated with a highest prediction score;
• execute, on an input content item with a first age rating in the source territory, a machine learning model that outputs a second age rating of the input content item in the target territory, wherein the machine learning model is trained using age ratings of at least the target territory, the territory associated with the highest prediction score and the source territory when the territory associated with the highest prediction score is not the source territory;
• and
• in response to determining that the second age rating exceeds a parental control threshold, block access the input content item.